Abstract

Cross-domain underwater object recognition is essential for intelligent visual monitoring in marine ranching, yet domain shift caused by varying water conditions and imaging devices severely degrades model performance. Existing adversarial domain adaptation methods align feature distributions to mitigate domain shift, but they often fail to preserve the fine-grained discriminative structure required for distinguishing visually similar marine species. Discriminator-free adversarial domain adaptation constrains only the sum of singular values, leading to projection distortion, thereby degrading target discriminability. We observe that the discriminative structure of the source domain is encoded in the singular value distribution of classifier outputs, and aligning this distribution across domains, rather than its sum, preserves discriminability during knowledge transfer. Based on this insight, we propose Adversarial Spectral Distribution Alignment (ASDA). ASDA consists of Spectral Distribution Alignment (SDA), which minimizes the Wasserstein distance between source and target singular value distributions, and a Dynamic Feature Queue (DFQ) with an adaptive length schedule that provides stable spectral distribution estimates across mini-batches. By enforcing singular value ratio consistency across all principal directions, SDA achieves fine-grained alignment, which reduces domain discrepancy while preserving discriminative features. Experimental results on two underwater image datasets demonstrate that ASDA outperforms existing domain adaptation methods.

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